AI-200 Practice Test 5 — 10 Questions

Question 1

Which THREE of the following items should be configured to ensure your AI workload in AKS is observable?

Accepted Answer

Prometheus and Grafana.. Logging, metrics, and tracing are the three pillars of observability in AKS.

Answer

Azure Backup.

Answer

Azure Bastion.

Question 2

Your enterprise requires that all container images deployed to Azure Kubernetes Service (AKS) must originate from approved corporate Azure Container Registries (ACRs). How can you enforce this restriction cluster-wide?

Accepted Answer

Configure Azure Policy for Kubernetes with the built-in initiative to restrict container registries. Azure Policy for Kubernetes uses Gatekeeper v3 to audit or enforce cluster behaviors, such as restricting image registries.

Answer

Configure standard Kubernetes Network Policies in the kube-system namespace

Answer

Set up ACR webhook notifications to trigger Azure Functions that delete unauthorized pods

Answer

Modify the kubelet configuration file on every cluster node manually

Question 3

When managing Azure Container Registry (ACR), administrators can use ACR Tasks to automate tasks. Which TWO scenarios are valid use cases for ACR Tasks? (Choose two.)

Accepted Answer

Triggering container image rebuilds automatically when a base image is updated. ACR Tasks supports automated container image builds on source code commits (CI) and automated OS/framework patching base image updates.

Answer

Providing a managed Kubernetes API control plane

Answer

Executing automated container vulnerability scans directly inside registry storage

Answer

Running a persistent, multi-node enterprise database cluster inside ACR

Question 4

Your development team is planning to deploy a containerized natural language processing model to Azure Container Apps. Which TWO features are natively supported by Azure Container Apps for managing application lifecycle and traffic? (Choose two.)

Accepted Answer

Built-in KEDA-based autoscaling including scaling to zero. Azure Container Apps natively supports multi-revision management and traffic splitting between revisions, as well as KEDA autoscaling.

Answer

Automatic deployment of bare-metal hypervisors

Answer

Direct root SSH access to individual container instances for interactive debugging

Answer

Direct manual configuration of control plane Kubernetes master nodes

Question 5

You are containerizing a large language model (LLM) inference API to run in Azure Container Apps. The model weights exceed 15 GB and cause container startup timeouts because the image takes too long to pull from Azure Container Registry (ACR). You need to optimize the container startup performance without reducing the model size. What is the best approach?

Accepted Answer

Enable ACR artifact streaming for the container registry.. ACR artifact streaming allows containers to start immediately by streaming layers on-demand without waiting for the entire image to download locally, significantly reducing cold-start times for large AI container images.

Answer

Upgrade the Azure Container Apps environment to use HTTP/2 protocol.

Answer

Store the model weights in an Azure Blob Storage account and mount them via an ephemeral volume.

Answer

Configure ACR content trust and geo-replication.

Question 6

You have a large image that takes a long time to pull. You want to speed up deployment in AKS. Which feature should you enable?

Accepted Answer

Enable Artifact Streaming in ACR.. Image Pull Secrets are standard, but ACR 'Artifact Streaming' is the specific feature for reducing pull times.

Answer

Use a public mirror for the image.

Answer

Increase the AKS node count.

Answer

Use smaller base images.

Question 7

Which TWO of the following are valid ways to reduce the size of a container image?

Accepted Answer

Use multi-stage Docker builds.. Multi-stage builds and using lighter base images are the two most effective strategies for image size reduction.

Answer

Use the 'latest' tag for all dependencies.

Answer

Enable ACR Geo-replication.

Answer

Install all dependencies in one RUN command.

Question 8

When designing an AI architecture that uses Azure AI Search, which TWO methods can be used to ingest documents and their vector embeddings into the search index? (Choose two)

Accepted Answer

Push API using HTTP POST requests to upload documents directly. Azure AI Search supports pushing data via the Push API (REST/SDK) or pulling data using indexers connected to data sources like Blob Storage or Cosmos DB.

Answer

Direct mounting of an NFS network share as the search index

Answer

FTP file transfer into the search engine storage directory

Answer

Direct SQL Server backup restore (.bak file import)

Question 9

You are designing a data layer architecture for an enterprise AI assistant that requires high-performance vector search, structured filtering, and multi-tenant isolation. Which THREE design patterns should you consider? (Choose three)

Accepted Answer

Combining vector similarity with metadata filters to narrow search scope. Effective AI data layer design patterns include logical partitioning for multi-tenancy, pre-filtering metadata, and hybrid retrieval combining keyword and vector search.

Answer

Storing all tenant data in a single unindexed JSON blob without partition keys

Answer

Hardcoding all user prompts directly into the search index schema

Question 10

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO steps are required to prepare a container for vector search before inserting documents? (Choose two)

Accepted Answer

Define the vectorEmbeddingPolicy at container creation or update. Preparing a Cosmos DB container for vector search requires adding a vector embedding policy and defining vector indexes in the indexing policy.

Answer

Mount an NFS network share for vector file storage

Answer

Install the pgvector extension on the container

Answer

Create a relational foreign key constraint to Azure SQL